This is an analysis of differential expression at the transcript level between treatment groups for control and infected blue-winged teal ileum samples.

## Load packages and data
library(limma)
library(edgeR)
library(Glimma)
library(gplots)
library(ggrepel)
library(RColorBrewer)
library(gridExtra)
library(kableExtra)
library(topGO)
library(clusterProfiler)
library(tidyverse)

## Load data
annot <- read.delim("G:/Shared drives/RNA Seq Supershedder Project/BWTE DE manuscript/extData/Trinotate.csv", header = TRUE, sep = "\t")
cnt <- read.table("G:/Shared drives/RNA Seq Supershedder Project/BWTE DE manuscript/extData/rsem.isoform.counts.matrix", header = TRUE)
covars <- read.csv("G:/Shared drives/RNA Seq Supershedder Project/BWTE DE manuscript/extData/BWTE54_SSgroup_Raw_Pool.csv", header = TRUE)

Clean data

Bird 36 is missing a measurement for the weight covariate and bird 19 had improper tissue-based grouping, suggesting a potential sample mix up. They are both removed from the analysis. We also rename the transcripts, dropping the “Trinity_” prefix.

cnt <- cnt %>%
  select(
    -alignEstimateAbundance_BWTE_Ileum_36_S50,
    -alignEstimateAbundance_BWTE_Bursa_36_S31,-alignEstimateAbundance_BWTE_Ileum_19_S35,
    -alignEstimateAbundance_BWTE_Bursa_19_S14) %>%
  rownames_to_column("transcript") %>%
  separate(transcript, into = c(NA, "pt1", "pt2", "gene", "isoform")) %>%
  unite(transcript, pt1, pt2, gene, isoform) %>%
  column_to_rownames("transcript") %>% 
  select(contains("Ileum"))

covars <- covars %>%
  filter(!bird %in% c("36", "19")) %>%
  arrange(bird) %>%
  mutate(group = str_remove(group, "-"))

annot <- annot %>%
  separate(transcript_id, into = c(NA, "pt1", "pt2", "gene", "isoform")) %>%
  unite(transcript_id, pt1, pt2, gene, isoform)

Set up model effects

bird <- as.factor(covars$bird)
sex <- as.factor(covars$sex)
age <- as.numeric(covars$age)
weight <- covars$wt_55
group <- recode(covars$group, C1 = "Ctl", C14 = "Ctl") %>% 
  as.factor()
pool <- as.factor(covars$Pool.Ileum)

Prep data for analysis

Here we standardize expression levels across samples of varying depth by using the counts per million (CPM) and trimmed mean of M-values (TMM) methods. We also remove any transcripts that are expressed at few than 0.5 CPM in at least 25% of individuals.

#Convert to DGEList object
dge <- DGEList(counts=cnt)
dge$genes <- annot[match(rownames(dge$counts), annot$transcript_id),]

#CPM and log-CPM
cpm <- cpm(dge)
lcpm <- cpm(dge, log = TRUE)

#### Retain only transcripts expressed at >0.5 CPM in at least 25% of individuals
table(rowSums(dge$counts==0) == length(cnt[1,]))
## 
##  FALSE   TRUE 
## 484888  86217
keep.exprs <- rowSums(cpm>0.5) >= length(cnt[1,])/4
sum(keep.exprs)
## [1] 66980
dge <- dge[keep.exprs,, keep.lib.sizes=FALSE]
table(rowSums(dge$counts==0) == length(cnt[1,]))
## 
## FALSE 
## 66980
dim(dge)
## [1] 66980    52
#TMM normalization
dge <- calcNormFactors(dge, method = "TMM")

Plot MDS

Multidimensional scaling (MDS) plots are an ordination technique that lets us examine sample clustering based on overall gene expression levels.

col.group = as.numeric(as.factor(paste0(group)))
plotMDS(lcpm, labels = paste0(bird, "_", group), col = col.group, cex = 0.75)
title(main="Treatment group")

Differential expression analysis

Establish design & contrast matrices

## Establish design matrix
design = model.matrix(~ 0 +
                        group +
                        age +
                        sex + 
                        weight + 
                        pool)

colnames(design) <- gsub("group", "", colnames(design))

contr.matrix = makeContrasts(
  CtlvI1 = Ctl - I1,
  CtlvI3 = Ctl - I3,
  CtlvI5 = Ctl - I5,
  CtlvI14 = Ctl - I14,
  I1vI3 = I1 - I3,
  I3vI5 = I3 - I5,
  I5vI14 = I5 - I14,
  levels = colnames(design))

Mean-variance trend and sample weight plots

v <- voomWithQualityWeights(dge, design, plot = TRUE)

Fitting the model

vfit <- lmFit(v, design) 
vfit <- contrasts.fit(vfit, contrasts = contr.matrix)
tfit <- treat(vfit, lfc = 1.0)
plotSA(tfit)

Count DE transcripts

For a transcript to be considered differentially expressed, we require a p-value of 0.1 with a false discovery rate correction and a log fold count difference of 1.

dt <- decideTests(tfit, p.value = 0.1, adjust.method = "fdr")
summary(dt) %>% 
  kable() %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = F)
CtlvI1 CtlvI3 CtlvI5 CtlvI14 I1vI3 I3vI5 I5vI14
Down 58 27 0 0 0 1 0
NotSig 66915 66947 66980 66979 66979 66979 66979
Up 7 6 0 1 1 0 1 
dt.tib <- as_tibble(dt, rownames = NA) %>% 
  rownames_to_column("transcript") %>% 
  mutate_at(vars(starts_with("C")), as.numeric) %>% 
  mutate_at(vars(starts_with("I")), as.numeric) %>% 
  filter_at(vars(CtlvI1:I5vI14), any_vars(. != 0))

Histogram of P-values

hist(tfit$p.value, main = "P-values comparing groups (eBayes)")

Volcano plot

tmp1 <- topTreat(tfit, coef = 1, n = Inf)
results1 <- mutate(tmp1, sig=ifelse(tmp1$adj.P.Val<0.1, "Sig", "Not Sig"))
p1 <- ggplot(results1, aes(logFC, -log10(P.Value))) +
  geom_point(aes(col = sig)) +
  scale_color_manual(values=c("black", "red")) +
  ggtitle("Control vs. Infected, Day 1") +
  ylab("-Log10(Adjusted p-value)") +
  xlab("Log(Fold count)") +
  theme_bw() +
  theme(legend.position = "none")

tmp2 <- topTreat(tfit, coef = 2, n = Inf)
results2 <- mutate(tmp2, sig=ifelse(tmp2$adj.P.Val<0.1, "Sig", "Not Sig"))
p2 <- ggplot(results2, aes(logFC, -log10(P.Value))) +
  geom_point(aes(col = sig)) +
  scale_color_manual(values=c("black", "red")) +
  ggtitle("Control vs. Infected, Day 3") +
  ylab("-Log10(Adjusted p-value)") +
  xlab("Log(Fold count)") +
  theme_bw() +
  theme(legend.position = "none")

tmp3 <- topTreat(tfit, coef = 3, n = Inf)
results3 <- mutate(tmp3, sig=ifelse(tmp3$adj.P.Val<0.1, "Sig", "Not Sig"))
p3 <- ggplot(results3, aes(logFC, -log10(P.Value))) +
  geom_point(aes(col = sig)) +
  scale_color_manual(values=c("black", "red")) +
  ggtitle("Control vs. Infected, Day 5") +
  ylab("-Log10(Adjusted p-value)") +
  xlab("Log(Fold count)") +
  theme_bw() +
  theme(legend.position = "none")

tmp4 <- topTreat(tfit, coef = 4, n = Inf)
results4 <- mutate(tmp4, sig=ifelse(tmp4$adj.P.Val<0.1, "Sig", "Not Sig"))
p4 <- ggplot(results4, aes(logFC, -log10(P.Value))) +
  geom_point(aes(col = sig)) +
  scale_color_manual(values=c("black", "red")) +
  ggtitle("Control vs. Infected, Day 14") +
  ylab("-Log10(Adjusted p-value)") +
  xlab("Log(Fold count)") +
  theme_bw() +
  theme(legend.position = "none")

tmp5 <- topTreat(tfit, coef = 5, n = Inf)
results5 <- mutate(tmp5, sig=ifelse(tmp5$adj.P.Val<0.1, "Sig", "Not Sig"))
p5 <- ggplot(results5, aes(logFC, -log10(P.Value))) +
  geom_point(aes(col = sig)) +
  scale_color_manual(values=c("black", "red")) +
  ggtitle("Infected, Day 1 vs. Infected, Day 3") +
  ylab("-Log10(Adjusted p-value)") +
  xlab("Log(Fold count)") +
  theme_bw() +
  theme(legend.position = "none")

tmp6 <- topTreat(tfit, coef = 6, n = Inf)
results6 <- mutate(tmp6, sig=ifelse(tmp6$adj.P.Val<0.1, "Sig", "Not Sig"))
p6 <- ggplot(results6, aes(logFC, -log10(P.Value))) +
  geom_point(aes(col = sig)) +
  scale_color_manual(values=c("black", "red")) +
  ggtitle("Infected, Day 3 vs. Infected, Day 5") +
  ylab("-Log10(Adjusted p-value)") +
  xlab("Log(Fold count)") +
  theme_bw() +
  theme(legend.position = "none")


tmp7 <- topTreat(tfit, coef = 7, n = Inf)
results7 <- mutate(tmp7, sig=ifelse(tmp7$adj.P.Val<0.1, "Sig", "Not Sig"))
p7 <- ggplot(results7, aes(logFC, -log10(P.Value))) +
  geom_point(aes(col = sig)) +
  scale_color_manual(values=c("black", "red")) +
  ggtitle("Infected, Day 5 vs. Infected, Day 14") +
  ylab("-Log10(Adjusted p-value)") +
  xlab("Log(Fold count)") +
  theme_bw() +
  theme(legend.position = "none")

grid.arrange(p1, p2, p3, p4, p5, p6, p7, nrow = 4)

Heatmap

# Get the gene names for DE genes
deGeneCounts <- lcpm %>%
  as_tibble(rownames = NA) %>%
  rownames_to_column("transcript") %>%
  filter(transcript %in% dt.tib$transcript) %>%
  column_to_rownames("transcript")

group2 <- group %>% 
  as_tibble() %>% 
  mutate(group = sub("I3", "I03", value)) %>% 
  mutate(group = sub("I1", "I01", group)) %>% 
  mutate(group = sub("I5", "I05", group)) %>% 
  mutate(group = sub("I014", "I14", group))

newNames1 <- colnames(deGeneCounts) %>%
  as_tibble() %>%
  separate(value, into = c(NA, NA, NA, "sample", NA)) %>%
  mutate(group = group2$group) %>%
  unite("sample", 2:1, sep = "_")

colnames(deGeneCounts) <- newNames1$sample
deGeneCounts <- deGeneCounts %>% dplyr::select(order(colnames(.))) %>%
  as.matrix()


heatmap.annot <- annot %>%
  select(
    transcript_id,
    sprot_Top_BLASTX_hit) %>% 
  filter(transcript_id %in% dt.tib$transcript) %>% 
  separate(sprot_Top_BLASTX_hit, into = c("SwissProt_GeneName", NA, NA, NA, NA, "sprot2", NA), "\\^") %>% 
  separate(sprot2, sep = "=", into = c(NA, "SwissProt_GeneFunction")) %>% 
  distinct(transcript_id, SwissProt_GeneName) %>% 
  filter(SwissProt_GeneName != ".")

deGeneCounts <- deGeneCounts %>% 
  as_tibble(rownames = NA) %>% 
  mutate(transcript = rownames(.)) %>% 
  rename(transcript_id = transcript) %>% 
  left_join(heatmap.annot) %>% 
  mutate(SwissProt_GeneName = replace_na(SwissProt_GeneName, ".")) %>% 
  unite(transcript, transcript_id:SwissProt_GeneName, sep = " - ") %>% 
  column_to_rownames("transcript") %>% 
  as.matrix()

## Set up palette
mypalette <- brewer.pal(11,"RdYlBu")
morecols <- colorRampPalette(mypalette)

hc <- hclust(as.dist(1-cor(t(deGeneCounts))))

# Plot the heatmap
heatmap.2(deGeneCounts,
          Colv = FALSE,
          Rowv = as.dendrogram(hc),
          col = rev(morecols(50)),
          trace = "none",
          colsep = c(9, 21, 33, 45),
          dendrogram = "row",
          density.info = "none",
          key = TRUE,
          #main = "Ileum - Gene level",
          margins = c(10, 15),
          scale ="row")

Boxplots for differentially expressed transcripts

lcpm.DE <- lcpm %>%
  as_tibble(rownames = NA) %>% 
  rownames_to_column("identifier") %>% 
  filter(identifier %in% dt.tib$transcript) %>%
  pivot_longer(cols = contains("_"),
               names_to = "sample",
               values_to = "lcpm") %>%
  separate(sample, into = c(NA, NA, "tissue", "bird", NA)) %>%
  mutate(bird = as.integer(bird)) %>%
  left_join(covars, by = "bird") %>%
  select(identifier, bird, lcpm, group) %>% 
  mutate(group = recode(group, C14 = "Control"),
         group = recode(group, C1 = "Control"),
         tmpID = ifelse(group == "Control", 'Control', 'Infected'))


aprioriPlotting <- function(target, ...) {
  annot.target <- annot %>%
    select(
      transcript_id,
      sprot_Top_BLASTX_hit) %>%
    filter(transcript_id == target) %>% 
    separate(sprot_Top_BLASTX_hit, into = c("SwissProt_GeneName", NA, NA, NA, NA, NA, NA), "\\^")
  
  plot <- lcpm.DE %>%
    filter(identifier == target) %>%
    mutate(group = fct_relevel(group, "Control", "I1", "I3", "I5", "I14")) %>%
    ggplot(aes(x = group, y = lcpm)) +
    facet_grid(. ~ tmpID, scales = "free", space = "free") +
    ylab("Log2(Counts per million)") +
    xlab("Group") +
    geom_point(position = position_dodge(width=0.75), show.legend = FALSE) +
    geom_boxplot(alpha = 0.5) +
    geom_label_repel(aes(label = bird, fill = NULL), show.legend=FALSE) +
    theme_classic() +
    labs(title= paste0(target, " - ", annot.target[1,2])) +
    theme(legend.title = element_blank())
  
  print(plot)
}

for(trans in sort(unique(lcpm.DE$identifier))) {
  aprioriPlotting(trans)
}

Annotations for differentially expressed transcripts

ns denotes non-significant transcripts for each comparison and numerical values are the log(fold count) difference

tmp1a <- tmp1 %>% select(transcript_id, adj.P.Val, logFC, sprot_Top_BLASTX_hit, Kegg) %>% separate(sprot_Top_BLASTX_hit, into = c("SwissProt_GeneName", NA, NA, NA, NA, "sprot2", NA), "\\^") %>%   separate(sprot2, sep = "=", into = c(NA, "SwissProt_GeneFunction")) %>%  separate(Kegg, into = c("KEGG_ID", "KO_ID"), sep = "\\`") %>%  mutate(comp = "CtlvI1")
tmp2a <- tmp2 %>% select(transcript_id, adj.P.Val, logFC, sprot_Top_BLASTX_hit, Kegg) %>% separate(sprot_Top_BLASTX_hit, into = c("SwissProt_GeneName", NA, NA, NA, NA, "sprot2", NA), "\\^") %>%   separate(sprot2, sep = "=", into = c(NA, "SwissProt_GeneFunction")) %>%  separate(Kegg, into = c("KEGG_ID", "KO_ID"), sep = "\\`") %>% mutate(comp = "CtlvI3")
tmp3a <- tmp3 %>% select(transcript_id, adj.P.Val, logFC, sprot_Top_BLASTX_hit, Kegg) %>% separate(sprot_Top_BLASTX_hit, into = c("SwissProt_GeneName", NA, NA, NA, NA, "sprot2", NA), "\\^") %>%   separate(sprot2, sep = "=", into = c(NA, "SwissProt_GeneFunction")) %>%  separate(Kegg, into = c("KEGG_ID", "KO_ID"), sep = "\\`") %>% mutate(comp = "CtlvI5")
tmp4a <- tmp4 %>% select(transcript_id, adj.P.Val, logFC, sprot_Top_BLASTX_hit, Kegg) %>% separate(sprot_Top_BLASTX_hit, into = c("SwissProt_GeneName", NA, NA, NA, NA, "sprot2", NA), "\\^") %>%   separate(sprot2, sep = "=", into = c(NA, "SwissProt_GeneFunction")) %>%  separate(Kegg, into = c("KEGG_ID", "KO_ID"), sep = "\\`") %>% mutate(comp = "CtlvI14")
tmp5a <- tmp5 %>% select(transcript_id, adj.P.Val, logFC, sprot_Top_BLASTX_hit, Kegg) %>% separate(sprot_Top_BLASTX_hit, into = c("SwissProt_GeneName", NA, NA, NA, NA, "sprot2", NA), "\\^") %>%   separate(sprot2, sep = "=", into = c(NA, "SwissProt_GeneFunction")) %>%  separate(Kegg, into = c("KEGG_ID", "KO_ID"), sep = "\\`") %>% mutate(comp = "I1vI3")
tmp6a <- tmp6 %>% select(transcript_id, adj.P.Val, logFC, sprot_Top_BLASTX_hit, Kegg) %>% separate(sprot_Top_BLASTX_hit, into = c("SwissProt_GeneName", NA, NA, NA, NA, "sprot2", NA), "\\^") %>%   separate(sprot2, sep = "=", into = c(NA, "SwissProt_GeneFunction")) %>%  separate(Kegg, into = c("KEGG_ID", "KO_ID"), sep = "\\`") %>% mutate(comp = "I3vI5")
tmp7a <- tmp7 %>% select(transcript_id, adj.P.Val, logFC, sprot_Top_BLASTX_hit, Kegg) %>% separate(sprot_Top_BLASTX_hit, into = c("SwissProt_GeneName", NA, NA, NA, NA, "sprot2", NA), "\\^") %>%   separate(sprot2, sep = "=", into = c(NA, "SwissProt_GeneFunction")) %>%  separate(Kegg, into = c("KEGG_ID", "KO_ID"), sep = "\\`") %>% mutate(comp = "I5vI14")

bind_rows(tmp1a, tmp2a, tmp3a, tmp4a, tmp5a, tmp6a, tmp7a) %>% 
  select(-SwissProt_GeneFunction, -KEGG_ID, -KO_ID) %>% 
  filter(transcript_id %in% dt.tib$transcript) %>% 
  mutate(logFoldCount = ifelse(adj.P.Val < 0.10, round(logFC, 2), "ns")) %>% 
  select(-adj.P.Val, -logFC) %>% 
  pivot_wider(names_from = comp, values_from = logFoldCount) %>% 
  kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
transcript_id SwissProt_GeneName CtlvI1 CtlvI3 CtlvI5 CtlvI14 I1vI3 I3vI5 I5vI14
DN2085_c0_g2_i5 MX_ANAPL -5.94 -4.77 ns ns ns ns ns
DN2085_c0_g2_i14 MX_ANAPL -7.23 -5.74 ns ns ns ns ns
DN2085_c0_g2_i11 MX_ANAPL -5.5 -4.05 ns ns ns ns ns
DN17179_c0_g1_i5 ESIP1_HUMAN -6.53 -5.37 ns ns ns ns ns
DN2085_c0_g2_i16 MX_ANAPL -5.24 -4.35 ns ns ns ns ns
DN614_c0_g2_i4 CMPK2_HUMAN -5.85 -4.19 ns ns ns ns ns
DN6590_c0_g1_i1 TMPS2_HUMAN -5.44 -4.07 ns ns ns ns ns
DN17179_c0_g1_i4 ESIP1_HUMAN -6.22 -5.84 ns ns ns ns ns
DN2085_c0_g2_i15 MX_ANAPL -7.03 -5.71 ns ns ns ns ns
DN428854_c0_g1_i1 . -4.93 ns ns ns ns ns ns
DN15897_c0_g1_i1 NCAP_I53A0 -11.68 -9.93 ns ns ns ns ns
DN8513_c3_g1_i1 M1_I80AD -10.91 -8.96 ns ns ns ns ns
DN2085_c0_g2_i10 MX_ANAPL -5.34 -4.19 ns ns ns ns ns
DN614_c0_g2_i1 CMPK2_HUMAN -5.77 ns ns ns ns ns ns
DN7314_c1_g2_i2 HEMA_I87A1 -10.57 -8.37 ns ns ns ns ns
DN7314_c1_g1_i2 HEMA_I87A1 -9.75 -7.65 ns ns ns ns ns
DN1765_c0_g1_i1 . -5.81 ns ns ns ns ns ns
DN43220_c0_g1_i9 UBP18_HUMAN -4.22 -3.58 ns ns ns ns ns
DN11336_c0_g1_i2 NS1_I78A1 -9.87 -8.37 ns ns ns ns ns
DN17179_c0_g1_i2 ESIP1_HUMAN -6.06 -4.92 ns ns ns ns ns
DN95654_c0_g1_i1 DSG1_PIG 4.47 ns ns ns ns ns ns
DN12097_c0_g2_i4 NRAM_I85A8 -9.24 -7.44 ns ns ns ns ns
DN8513_c3_g2_i1 M2_I66A0 -9.53 -7.71 ns ns ns ns ns
DN17249_c1_g1_i1 TMPS2_HUMAN -5.14 ns ns ns ns ns ns
DN3062_c0_g1_i18 RN213_HUMAN -4.59 ns ns ns ns ns ns
DN2198_c0_g1_i1 DDX60_HUMAN -5.73 -4.75 ns ns ns ns ns
DN2085_c0_g2_i13 MX_ANAPL -5.11 ns ns ns ns ns ns
DN591_c9_g2_i1 . -5.12 ns ns ns ns ns ns
DN14251_c0_g1_i13 . -5.83 ns ns ns ns ns ns
DN2198_c0_g1_i6 DDX60_HUMAN -4.54 -3.98 ns ns ns ns ns
DN6178_c0_g1_i1 IFIT5_HUMAN -5.06 ns ns ns ns ns ns
DN2932_c0_g1_i6 IFI6_BOVIN -4.77 -5.45 ns ns ns ns ns
DN1920_c0_g1_i9 RSAD2_BOVIN -4.72 ns ns ns ns ns ns
DN2085_c0_g2_i12 MX_ANAPL -5.43 ns ns ns ns ns ns
DN10817_c0_g1_i1 RDRP_I78AC -8.07 ns ns ns ns ns ns
DN2198_c0_g1_i10 DDX60_HUMAN -3.97 -3.61 ns ns ns ns ns
DN3062_c0_g1_i12 RN213_HUMAN -3.17 ns ns ns ns ns ns
DN2559_c0_g1_i7 MOV10_CHICK -5.23 ns ns ns ns ns ns
DN7633_c1_g1_i16 BIRC6_HUMAN 7.74 ns ns ns ns ns ns
DN1920_c0_g1_i11 RSAD2_PIG -4.74 ns ns ns ns ns ns
DN17714_c0_g1_i3 PA_I78AC -7.4 ns ns ns ns ns ns
DN4591_c0_g1_i4 SAM9L_HUMAN -3.16 ns ns ns ns ns ns
DN614_c0_g2_i8 CMPK2_HUMAN -4.6 ns ns ns ns ns ns
DN2145_c1_g1_i1 . -5.59 ns ns ns ns ns ns
DN9487_c0_g1_i2 . -4.44 ns ns ns ns ns ns
DN427_c0_g2_i4 NOX4_PONAB 2.88 ns ns ns ns ns ns
DN135990_c0_g1_i1 PB2_I80A6 -7.21 ns ns ns ns ns ns
DN2085_c0_g2_i6 MX_ANAPL -5.3 ns ns ns ns ns ns
DN9051_c0_g1_i3 GA2L1_MOUSE -5.92 ns ns ns ns ns ns
DN20874_c0_g1_i11 PAR12_HUMAN -2.44 ns ns ns ns ns ns
DN22_c0_g1_i2 MIRO1_CHICK -5.16 ns ns ns ns ns ns
DN1920_c0_g1_i1 RSAD2_BOVIN -6.25 ns ns ns ns ns ns
DN6190_c0_g1_i4 OASL2_MOUSE -5.23 ns ns ns ns ns ns
DN2767_c0_g1_i1 ISLR_HUMAN -5.75 ns ns ns ns ns ns
DN3062_c0_g1_i10 RN213_HUMAN -5.96 ns ns ns ns ns ns
DN24620_c0_g1_i11 . 3.66 ns ns ns ns ns ns
DN6309_c0_g1_i12 . -3.66 ns ns ns ns ns ns
DN166_c0_g1_i5 RN141_CHICK 7.59 ns ns ns ns ns ns
DN43220_c0_g1_i4 UBP18_HUMAN -5.61 ns ns ns ns ns ns
DN34132_c0_g1_i4 K2C74_MOUSE 3.55 ns ns ns ns ns ns
DN1934_c0_g1_i11 IRF3_CHICK -2.69 ns ns ns ns ns ns
DN17171_c0_g1_i3 MTAP2_RAT 4.29 ns ns ns ns ns ns
DN2293_c0_g1_i3 PIGY_HUMAN -5.69 ns ns ns ns ns ns
DN6190_c0_g1_i2 OASL1_RAT -3.73 ns ns ns ns ns ns
DN1934_c0_g1_i16 IRF3_CHICK -4.55 ns ns ns ns ns ns
DN5214_c0_g1_i6 CF062_MOUSE ns -4.76 ns ns ns ns ns
DN9772_c0_g1_i10 UQCC2_DANRE ns -4.15 ns ns ns ns ns
DN2228_c2_g1_i8 BMPR2_MOUSE ns 5.66 ns ns ns ns ns
DN4998_c0_g1_i2 CNPY3_BOVIN ns -3.85 ns ns ns ns ns
DN14729_c0_g1_i4 FREM1_HUMAN ns 4.62 ns ns ns ns ns
DN4239_c0_g1_i17 MYCB2_MOUSE ns 5.72 ns ns ns ns ns
DN220085_c0_g1_i2 LAG3_MOUSE ns -3.47 ns ns ns ns ns
DN12331_c1_g1_i3 LRIG3_HUMAN ns 4.28 ns ns ns ns ns
DN611_c1_g1_i6 LRRK2_HUMAN ns 4.31 ns ns ns ns ns
DN2458_c0_g1_i1 DDX17_MOUSE ns 4.36 ns ns 4.87 ns ns
DN1712_c0_g2_i10 DCAF4_HUMAN ns ns ns 4.94 ns ns 4.84
DN3176_c0_g1_i11 . ns ns ns ns ns -3.59 ns

Transcript functions

bind_rows(tmp1a, tmp2a, tmp3a, tmp4a, tmp5a, tmp6a, tmp7a) %>% 
  select(-comp, -adj.P.Val, -logFC) %>% 
  filter(transcript_id %in% dt.tib$transcript) %>% 
  distinct(.) %>% filter(SwissProt_GeneName != ".") %>% 
  kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
transcript_id SwissProt_GeneName SwissProt_GeneFunction KEGG_ID KO_ID
DN2085_c0_g2_i5 MX_ANAPL Interferon-induced GTP-binding protein Mx; KEGG:apla:101793492 KO:K14754
DN2085_c0_g2_i14 MX_ANAPL Interferon-induced GTP-binding protein Mx; KEGG:apla:101793492 KO:K14754
DN2085_c0_g2_i11 MX_ANAPL Interferon-induced GTP-binding protein Mx; KEGG:apla:101793492 KO:K14754
DN17179_c0_g1_i5 ESIP1_HUMAN Epithelial-stromal interaction protein 1; KEGG:hsa:94240 NA
DN2085_c0_g2_i16 MX_ANAPL Interferon-induced GTP-binding protein Mx; KEGG:apla:101793492 KO:K14754
DN614_c0_g2_i4 CMPK2_HUMAN UMP-CMP kinase 2, mitochondrial; KEGG:hsa:129607 KO:K13809
DN6590_c0_g1_i1 TMPS2_HUMAN Transmembrane protease serine 2; KEGG:hsa:7113 KO:K09633
DN17179_c0_g1_i4 ESIP1_HUMAN Epithelial-stromal interaction protein 1; KEGG:hsa:94240 NA
DN2085_c0_g2_i15 MX_ANAPL Interferon-induced GTP-binding protein Mx; KEGG:apla:101793492 KO:K14754
DN15897_c0_g1_i1 NCAP_I53A0 Nucleoprotein {ECO:0000255|HAMAP-Rule:MF_04070}; . NA
DN8513_c3_g1_i1 M1_I80AD Matrix protein 1 {ECO:0000255|HAMAP-Rule:MF_04068}; . NA
DN2085_c0_g2_i10 MX_ANAPL Interferon-induced GTP-binding protein Mx; KEGG:apla:101793492 KO:K14754
DN614_c0_g2_i1 CMPK2_HUMAN UMP-CMP kinase 2, mitochondrial; KEGG:hsa:129607 KO:K13809
DN7314_c1_g2_i2 HEMA_I87A1 Hemagglutinin {ECO:0000255|HAMAP-Rule:MF_04072}; . NA
DN7314_c1_g1_i2 HEMA_I87A1 Hemagglutinin {ECO:0000255|HAMAP-Rule:MF_04072}; . NA
DN43220_c0_g1_i9 UBP18_HUMAN Ubl carboxyl-terminal hydrolase 18; KEGG:hsa:11274 KO:K11846
DN11336_c0_g1_i2 NS1_I78A1 Non-structural protein 1 {ECO:0000255|HAMAP-Rule:MF_04066}; . NA
DN17179_c0_g1_i2 ESIP1_HUMAN Epithelial-stromal interaction protein 1; KEGG:hsa:94240 NA
DN95654_c0_g1_i1 DSG1_PIG Desmoglein-1; KEGG:ssc:641355 KO:K07596
DN12097_c0_g2_i4 NRAM_I85A8 Neuraminidase {ECO:0000255|HAMAP-Rule:MF_04071}; . NA
DN8513_c3_g2_i1 M2_I66A0 Matrix protein 2 {ECO:0000255|HAMAP-Rule:MF_04069}; . NA
DN17249_c1_g1_i1 TMPS2_HUMAN Transmembrane protease serine 2; KEGG:hsa:7113 KO:K09633
DN3062_c0_g1_i18 RN213_HUMAN E3 ubiquitin-protein ligase RNF213 {ECO:0000305}; KEGG:hsa:57674 KO:K22754
DN2198_c0_g1_i1 DDX60_HUMAN Probable ATP-dependent RNA helicase DDX60; KEGG:hsa:55601 KO:K20103
DN2085_c0_g2_i13 MX_ANAPL Interferon-induced GTP-binding protein Mx; KEGG:apla:101793492 KO:K14754
DN2198_c0_g1_i6 DDX60_HUMAN Probable ATP-dependent RNA helicase DDX60; KEGG:hsa:55601 KO:K20103
DN6178_c0_g1_i1 IFIT5_HUMAN Interferon-induced protein with tetratricopeptide repeats 5; KEGG:hsa:24138 NA
DN2932_c0_g1_i6 IFI6_BOVIN Interferon alpha-inducible protein 6 {ECO:0000305}; KEGG:bta:512913 NA
DN1920_c0_g1_i9 RSAD2_BOVIN Radical S-adenosyl methionine domain-containing protein 2; KEGG:bta:506415 KO:K15045
DN2085_c0_g2_i12 MX_ANAPL Interferon-induced GTP-binding protein Mx; KEGG:apla:101793492 KO:K14754
DN10817_c0_g1_i1 RDRP_I78AC RNA-directed RNA polymerase catalytic subunit {ECO:0000255|HAMAP-Rule:MF_04065}; . NA
DN2198_c0_g1_i10 DDX60_HUMAN Probable ATP-dependent RNA helicase DDX60; KEGG:hsa:55601 KO:K20103
DN3062_c0_g1_i12 RN213_HUMAN E3 ubiquitin-protein ligase RNF213 {ECO:0000305}; KEGG:hsa:57674 KO:K22754
DN2559_c0_g1_i7 MOV10_CHICK Putative helicase MOV-10; KEGG:gga:419872 KO:K18422
DN7633_c1_g1_i16 BIRC6_HUMAN Baculoviral IAP repeat-containing protein 6; KEGG:hsa:57448 KO:K10586
DN1920_c0_g1_i11 RSAD2_PIG Radical S-adenosyl methionine domain-containing protein 2; KEGG:ssc:396752 KO:K15045
DN17714_c0_g1_i3 PA_I78AC Polymerase acidic protein {ECO:0000255|HAMAP-Rule:MF_04063}; . NA
DN4591_c0_g1_i4 SAM9L_HUMAN Sterile alpha motif domain-containing protein 9-like; KEGG:hsa:219285 NA
DN614_c0_g2_i8 CMPK2_HUMAN UMP-CMP kinase 2, mitochondrial; KEGG:hsa:129607 KO:K13809
DN427_c0_g2_i4 NOX4_PONAB NADPH oxidase 4; KEGG:pon:100171782 KO:K21423
DN135990_c0_g1_i1 PB2_I80A6 Polymerase basic protein 2 {ECO:0000255|HAMAP-Rule:MF_04062}; . NA
DN2085_c0_g2_i6 MX_ANAPL Interferon-induced GTP-binding protein Mx; KEGG:apla:101793492 KO:K14754
DN9051_c0_g1_i3 GA2L1_MOUSE GAS2-like protein 1; KEGG:mmu:78926 NA
DN20874_c0_g1_i11 PAR12_HUMAN Protein mono-ADP-ribosyltransferase PARP12 {ECO:0000305}; KEGG:hsa:64761 KO:K15259
DN22_c0_g1_i2 MIRO1_CHICK Mitochondrial Rho GTPase 1; KEGG:gga:417410 KO:K07870
DN1920_c0_g1_i1 RSAD2_BOVIN Radical S-adenosyl methionine domain-containing protein 2; KEGG:bta:506415 KO:K15045
DN6190_c0_g1_i4 OASL2_MOUSE 2’-5’-oligoadenylate synthase-like protein 2; KEGG:rno:304545 KO:K14608
DN2767_c0_g1_i1 ISLR_HUMAN Immunoglobulin superfamily containing leucine-rich repeat protein; KEGG:hsa:3671 NA
DN3062_c0_g1_i10 RN213_HUMAN E3 ubiquitin-protein ligase RNF213 {ECO:0000305}; KEGG:hsa:57674 KO:K22754
DN166_c0_g1_i5 RN141_CHICK RING finger protein 141; KEGG:gga:423039 NA
DN43220_c0_g1_i4 UBP18_HUMAN Ubl carboxyl-terminal hydrolase 18; KEGG:hsa:11274 KO:K11846
DN34132_c0_g1_i4 K2C74_MOUSE Keratin, type II cytoskeletal 74 {ECO:0000250|UniProtKB:Q7RT57}; KEGG:mmu:223917 KO:K07605
DN1934_c0_g1_i11 IRF3_CHICK Interferon regulatory factor 3; KEGG:gga:396330 KO:K09447
DN17171_c0_g1_i3 MTAP2_RAT Microtubule-associated protein 2; . NA
DN2293_c0_g1_i3 PIGY_HUMAN Phosphatidylinositol N-acetylglucosaminyltransferase subunit Y; KEGG:hsa:84992 KO:K11001
DN6190_c0_g1_i2 OASL1_RAT 2’-5’-oligoadenylate synthase-like protein 1; KEGG:rno:304545 KO:K14608
DN1934_c0_g1_i16 IRF3_CHICK Interferon regulatory factor 3; KEGG:gga:396330 KO:K09447
DN5214_c0_g1_i6 CF062_MOUSE Uncharacterized protein C6orf62 homolog; KEGG:mmu:79555 NA
DN9772_c0_g1_i10 UQCC2_DANRE Ubiquinol-cytochrome-c reductase complex assembly factor 2; KEGG:dre:393731 KO:K17682
DN2228_c2_g1_i8 BMPR2_MOUSE Bone morphogenetic protein receptor type-2; KEGG:mmu:12168 KO:K04671
DN4998_c0_g1_i2 CNPY3_BOVIN Protein canopy homolog 3; KEGG:bta:510220 KO:K22816
DN14729_c0_g1_i4 FREM1_HUMAN FRAS1-related extracellular matrix protein 1 {ECO:0000305}; KEGG:hsa:158326 KO:K23380
DN4239_c0_g1_i17 MYCB2_MOUSE E3 ubiquitin-protein ligase MYCBP2 {ECO:0000305}; KEGG:mmu:105689 KO:K10693
DN220085_c0_g1_i2 LAG3_MOUSE Lymphocyte activation gene 3 protein {ECO:0000303|PubMed:8921170}; KEGG:hsa:3902 KO:K06565
DN12331_c1_g1_i3 LRIG3_HUMAN Leucine-rich repeats and immunoglobulin-like domains protein 3; KEGG:hsa:121227 NA
DN611_c1_g1_i6 LRRK2_HUMAN Leucine-rich repeat serine/threonine-protein kinase 2; KEGG:hsa:120892 KO:K08844
DN2458_c0_g1_i1 DDX17_MOUSE Probable ATP-dependent RNA helicase DDX17; KEGG:mmu:67040 KO:K13178
DN1712_c0_g2_i10 DCAF4_HUMAN DDB1- and CUL4-associated factor 4; KEGG:bta:511629 KO:K11799

Gene Ontology Enrichment Analysis

topGO.mappings <- readMappings("G:/Shared drives/RNA Seq Supershedder Project/BWTE DE manuscript/extData/GOdbTrans.txt", sep = "\t", IDsep = ",")

goEnrich <- function(targetComp, ...) {
  DE.results <- dt.tib %>% filter(!!sym(targetComp) != 0)
  
  all.genes <- sort(unique(as.character(rownames(tfit$p.value))))
  int.genes <- DE.results$transcript
  int.genes <- factor(as.integer(all.genes %in% int.genes))
  names(int.genes) = all.genes
  
  go.obj.BP <- new("topGOdata", ontology='BP',
              allGenes = int.genes,
              annot = annFUN.gene2GO,
              nodeSize = 5,
              gene2GO = topGO.mappings)

  go.obj.MF <- new("topGOdata", ontology='MF',
                 allGenes = int.genes,
                 annot = annFUN.gene2GO,
                 nodeSize = 5,
                 gene2GO = topGO.mappings)

  go.obj.CC <- new("topGOdata", ontology='CC',
                 allGenes = int.genes,
                 annot = annFUN.gene2GO,
                 nodeSize = 5,
                 gene2GO = topGO.mappings)
  
  results.BP <- runTest(go.obj.BP, algorithm = "elim", statistic = "fisher")

  results.tab.BP <- GenTable(object = go.obj.BP, 
                          elimFisher = results.BP, 
                          topNodes = 50) %>% 
    as_tibble() %>% 
    mutate(pVal = as.numeric(elimFisher)) %>% 
    filter(pVal < 0.01) %>% 
    mutate(Domain = "BP") %>% 
    mutate(Comparison = targetComp)
  
  results.MF <- runTest(go.obj.MF, algorithm = "elim", statistic = "fisher")
  
  results.tab.MF <- GenTable(object = go.obj.MF, 
                          elimFisher = results.MF, 
                          topNodes = 50) %>% 
    as_tibble() %>% 
    mutate(pVal = as.numeric(elimFisher)) %>% 
    filter(pVal < 0.01) %>% 
    mutate(Domain = "MF") %>% 
    mutate(Comparison = targetComp)
  
  results.CC <- runTest(go.obj.CC, algorithm = "elim", statistic = "fisher")
  
  results.tab.CC <- GenTable(object = go.obj.CC, 
                          elimFisher = results.CC, 
                          topNodes = 50) %>% 
    as_tibble() %>% 
    mutate(pVal = as.numeric(elimFisher)) %>% 
    filter(pVal < 0.01) %>% 
    mutate(Domain = "CC") %>% 
    mutate(Comparison = targetComp)
  
  rbind(results.tab.BP,
    results.tab.MF,
    results.tab.CC)
}

results <- list()

for(z in unique(names(dt.tib)[-1])) {
  if (dt.tib %>% 
      filter(!!sym(z) != 0) %>% 
      nrow(.) > 0)
  results[[length(results)+1]] <- goEnrich(z)
}

results.tib <- bind_rows(results, .id = "column_label") %>%
  select(-column_label) %>% 
  arrange(GO.ID, Comparison)

kable(results.tib) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
GO.ID Term Annotated Significant Expected elimFisher pVal Domain Comparison
GO:0000381 regulation of alternative mRNA splicing,… 267 1 0.01 0.0062 6.20e-03 BP I1vI3
GO:0001837 epithelial to mesenchymal transition 352 1 0.01 0.0082 8.20e-03 BP I1vI3
GO:0002040 sprouting angiogenesis 319 3 0.34 0.00462 4.62e-03 BP CtlvI1
GO:0003186 tricuspid valve morphogenesis 10 1 0.01 0.00536 5.36e-03 BP CtlvI3
GO:0003252 negative regulation of cell proliferatio… 10 1 0.01 0.00536 5.36e-03 BP CtlvI3
GO:0003724 RNA helicase activity 317 4 0.40 0.00069 6.90e-04 MF CtlvI1
GO:0003724 RNA helicase activity 317 4 0.21 5.2e-05 5.20e-05 MF CtlvI3
GO:0003724 RNA helicase activity 317 1 0.01 0.0077 7.70e-03 MF I1vI3
GO:0003725 double-stranded RNA binding 250 5 0.32 1.6e-05 1.60e-05 MF CtlvI1
GO:0003725 double-stranded RNA binding 250 3 0.16 0.00058 5.80e-04 MF CtlvI3
GO:0003727 single-stranded RNA binding 335 4 0.42 0.00085 8.50e-04 MF CtlvI1
GO:0003727 single-stranded RNA binding 335 3 0.22 0.00134 1.34e-03 MF CtlvI3
GO:0003924 GTPase activity 866 10 1.09 1.1e-07 1.00e-07 MF CtlvI1
GO:0003924 GTPase activity 866 7 0.57 1.1e-06 1.10e-06 MF CtlvI3
GO:0003968 RNA-directed 5’-3’ RNA polymerase activi… 11 2 0.01 8.5e-05 8.50e-05 MF CtlvI1
GO:0004127 cytidylate kinase activity 12 3 0.02 4.1e-07 4.00e-07 MF CtlvI1
GO:0004127 cytidylate kinase activity 12 1 0.01 0.00783 7.83e-03 MF CtlvI3
GO:0004550 nucleoside diphosphate kinase activity 41 3 0.05 1.9e-05 1.90e-05 MF CtlvI1
GO:0004798 thymidylate kinase activity 9 3 0.01 1.6e-07 2.00e-07 MF CtlvI1
GO:0004798 thymidylate kinase activity 9 1 0.01 0.00588 5.88e-03 MF CtlvI3
GO:0005026 transforming growth factor beta receptor… 10 1 0.01 0.00653 6.53e-03 MF CtlvI3
GO:0005525 GTP binding 1111 10 1.40 1.1e-06 1.10e-06 MF CtlvI1
GO:0005525 GTP binding 1111 7 0.73 5.6e-06 5.60e-06 MF CtlvI3
GO:0005811 lipid droplet 227 3 0.27 0.00251 2.51e-03 CC CtlvI1
GO:0006165 nucleoside diphosphate phosphorylation 359 3 0.38 0.00640 6.40e-03 BP CtlvI1
GO:0006227 dUDP biosynthetic process 9 3 0.01 9.1e-08 1.00e-07 BP CtlvI1
GO:0006227 dUDP biosynthetic process 9 1 0.00 0.00483 4.83e-03 BP CtlvI3
GO:0006233 dTDP biosynthetic process 9 3 0.01 9.1e-08 1.00e-07 BP CtlvI1
GO:0006233 dTDP biosynthetic process 9 1 0.00 0.00483 4.83e-03 BP CtlvI3
GO:0006235 dTTP biosynthetic process 11 3 0.01 1.8e-07 2.00e-07 BP CtlvI1
GO:0006235 dTTP biosynthetic process 11 1 0.01 0.00590 5.90e-03 BP CtlvI3
GO:0007026 negative regulation of microtubule depol… 114 2 0.12 0.00646 6.46e-03 BP CtlvI1
GO:0009041 uridylate kinase activity 14 3 0.02 6.8e-07 7.00e-07 MF CtlvI1
GO:0009041 uridylate kinase activity 14 1 0.01 0.00913 9.13e-03 MF CtlvI3
GO:0010507 negative regulation of autophagy 214 2 0.11 0.00587 5.87e-03 BP CtlvI3
GO:0010586 miRNA metabolic process 111 1 0.00 0.0026 2.60e-03 BP I1vI3
GO:0014916 regulation of lung blood pressure 7 1 0.00 0.00376 3.76e-03 BP CtlvI3
GO:0016540 protein autoprocessing 39 2 0.04 0.00078 7.80e-04 BP CtlvI1
GO:0019012 virion 131 7 0.16 0.00466 4.66e-03 CC CtlvI1
GO:0019031 viral envelope 33 2 0.04 0.00071 7.10e-04 CC CtlvI1
GO:0019031 viral envelope 33 2 0.02 0.0002 2.00e-04 CC CtlvI3
GO:0019056 modulation by virus of host transcriptio… 5 3 0.01 1.1e-08 0.00e+00 BP CtlvI1
GO:0019062 virion attachment to host cell 43 2 0.05 0.00095 9.50e-04 BP CtlvI1
GO:0019062 virion attachment to host cell 43 2 0.02 0.00025 2.50e-04 BP CtlvI3
GO:0019064 fusion of virus membrane with host plasm… 33 2 0.03 0.00056 5.60e-04 BP CtlvI1
GO:0019064 fusion of virus membrane with host plasm… 33 2 0.02 0.00014 1.40e-04 BP CtlvI3
GO:0019065 receptor-mediated endocytosis of virus b… 10 2 0.01 4.8e-05 4.80e-05 BP CtlvI1
GO:0019065 receptor-mediated endocytosis of virus b… 10 2 0.01 1.2e-05 1.20e-05 BP CtlvI3
GO:0019076 viral release from host cell 98 4 0.10 3.6e-06 3.60e-06 BP CtlvI1
GO:0019076 viral release from host cell 98 4 0.05 2.2e-07 2.00e-07 BP CtlvI3
GO:0019083 viral transcription 296 3 0.31 0.00375 3.75e-03 BP CtlvI1
GO:0020002 host cell plasma membrane 94 4 0.11 5.0e-06 5.00e-06 CC CtlvI1
GO:0020002 host cell plasma membrane 94 4 0.06 3.9e-07 4.00e-07 CC CtlvI3
GO:0021785 branchiomotor neuron axon guidance 15 1 0.01 0.00803 8.03e-03 BP CtlvI3
GO:0022028 tangential migration from the subventric… 17 1 0.01 0.00910 9.10e-03 BP CtlvI3
GO:0030430 host cell cytoplasm 102 3 0.12 0.00025 2.50e-04 CC CtlvI1
GO:0030520 intracellular estrogen receptor signalin… 135 1 0.00 0.0032 3.20e-03 BP I1vI3
GO:0030521 androgen receptor signaling pathway 169 1 0.00 0.0039 3.90e-03 BP I1vI3
GO:0030683 mitigation of host immune response by vi… 6 1 0.01 0.00629 6.29e-03 BP CtlvI1
GO:0030683 mitigation of host immune response by vi… 6 1 0.00 0.00322 3.22e-03 BP CtlvI3
GO:0032473 cytoplasmic side of mitochondrial outer … 7 1 0.00 0.0044 4.40e-03 CC CtlvI3
GO:0032474 otolith morphogenesis 18 1 0.01 0.00963 9.63e-03 BP CtlvI3
GO:0034154 toll-like receptor 7 signaling pathway 19 3 0.02 1.0e-06 1.00e-06 BP CtlvI1
GO:0034165 positive regulation of toll-like recepto… 11 3 0.01 1.8e-07 2.00e-07 BP CtlvI1
GO:0034211 GTP-dependent protein kinase activity 8 1 0.01 0.00522 5.22e-03 MF CtlvI3
GO:0035279 mRNA cleavage involved in gene silencing… 8 1 0.01 0.00838 8.38e-03 BP CtlvI1
GO:0035564 regulation of kidney size 11 1 0.01 0.00590 5.90e-03 BP CtlvI3
GO:0035666 TRIF-dependent toll-like receptor signal… 59 2 0.06 0.00178 1.78e-03 BP CtlvI1
GO:0035712 T-helper 2 cell activation 7 1 0.01 0.00734 7.34e-03 BP CtlvI1
GO:0035713 response to nitrogen dioxide 6 1 0.01 0.00629 6.29e-03 BP CtlvI1
GO:0036122 BMP binding 13 1 0.01 0.00848 8.48e-03 MF CtlvI3
GO:0036479 peroxidase inhibitor activity 5 1 0.00 0.00327 3.27e-03 MF CtlvI3
GO:0039507 suppression by virus of host molecular f… 14 4 0.01 1.1e-09 0.00e+00 BP CtlvI1
GO:0039513 suppression by virus of host catalytic a… 10 1 0.01 0.00536 5.36e-03 BP CtlvI3
GO:0039519 modulation by virus of host autophagy 9 1 0.01 0.00942 9.42e-03 BP CtlvI1
GO:0039519 modulation by virus of host autophagy 9 1 0.00 0.00483 4.83e-03 BP CtlvI3
GO:0039657 suppression by virus of host gene expres… 25 4 0.03 1.3e-08 0.00e+00 BP CtlvI1
GO:0039694 viral RNA genome replication 103 3 0.11 0.00018 1.80e-04 BP CtlvI1
GO:0039706 co-receptor binding 9 1 0.01 0.00588 5.88e-03 MF CtlvI3
GO:0042025 host cell nucleus 34 6 0.04 2.7e-12 0.00e+00 CC CtlvI1
GO:0042025 host cell nucleus 34 3 0.02 1.4e-06 1.40e-06 CC CtlvI3
GO:0042289 MHC class II protein binding 11 1 0.01 0.00718 7.18e-03 MF CtlvI3
GO:0043367 CD4-positive, alpha-beta T cell differen… 239 4 0.25 0.00012 1.20e-04 BP CtlvI1
GO:0043382 positive regulation of memory T cell dif… 5 1 0.01 0.00525 5.25e-03 BP CtlvI1
GO:0043621 protein self-association 175 3 0.22 0.00142 1.42e-03 MF CtlvI1
GO:0044753 amphisome 9 1 0.01 0.0057 5.70e-03 CC CtlvI3
GO:0045071 negative regulation of viral genome repl… 136 5 0.14 3.3e-07 3.00e-07 BP CtlvI1
GO:0045085 negative regulation of interleukin-2 bio… 13 1 0.01 0.00696 6.96e-03 BP CtlvI3
GO:0045087 innate immune response 1505 13 1.58 8.1e-07 8.00e-07 BP CtlvI1
GO:0045087 innate immune response 1505 8 0.81 4.5e-05 4.50e-05 BP CtlvI3
GO:0045111 intermediate filament cytoskeleton 311 4 0.37 0.00052 5.20e-04 CC CtlvI1
GO:0045111 intermediate filament cytoskeleton 311 3 0.20 0.0010 1.00e-03 CC CtlvI3
GO:0045445 myoblast differentiation 227 1 0.01 0.0053 5.30e-03 BP I1vI3
GO:0045590 negative regulation of regulatory T cell… 8 1 0.01 0.00838 8.38e-03 BP CtlvI1
GO:0045590 negative regulation of regulatory T cell… 8 1 0.00 0.00429 4.29e-03 BP CtlvI3
GO:0045906 negative regulation of vasoconstriction 12 1 0.01 0.00643 6.43e-03 BP CtlvI3
GO:0046598 positive regulation of viral entry into … 28 2 0.03 0.00040 4.00e-04 BP CtlvI1
GO:0046718 viral entry into host cell 262 4 0.14 0.00523 5.23e-03 BP CtlvI3
GO:0046755 viral budding 74 4 0.08 1.2e-06 1.20e-06 BP CtlvI1
GO:0046755 viral budding 74 4 0.04 7.1e-08 1.00e-07 BP CtlvI3
GO:0048295 positive regulation of isotype switching… 8 1 0.01 0.00838 8.38e-03 BP CtlvI1
GO:0048842 positive regulation of axon extension in… 15 1 0.01 0.00803 8.03e-03 BP CtlvI3
GO:0050709 negative regulation of protein secretion 312 3 0.33 0.00434 4.34e-03 BP CtlvI1
GO:0051024 positive regulation of immunoglobulin se… 7 1 0.01 0.00734 7.34e-03 BP CtlvI1
GO:0051539 4 iron, 4 sulfur cluster binding 170 3 0.21 0.00131 1.31e-03 MF CtlvI1
GO:0051607 defense response to virus 526 12 0.55 1.1e-09 0.00e+00 BP CtlvI1
GO:0051865 protein autoubiquitination 241 4 0.25 0.00012 1.20e-04 BP CtlvI1
GO:0051900 regulation of mitochondrial depolarizati… 42 2 0.02 0.00023 2.30e-04 BP CtlvI3
GO:0052027 modulation by symbiont of host signal tr… 6 1 0.01 0.00629 6.29e-03 BP CtlvI1
GO:0052027 modulation by symbiont of host signal tr… 6 1 0.00 0.00322 3.22e-03 BP CtlvI3
GO:0055036 virion membrane 32 5 0.04 3.8e-10 0.00e+00 CC CtlvI1
GO:0055036 virion membrane 32 5 0.02 1.4e-11 0.00e+00 CC CtlvI3
GO:0060161 positive regulation of dopamine receptor… 13 1 0.01 0.00696 6.96e-03 BP CtlvI3
GO:0060338 regulation of type I interferon-mediated… 100 3 0.11 0.00016 1.60e-04 BP CtlvI1
GO:0060338 regulation of type I interferon-mediated… 100 2 0.05 0.00132 1.32e-03 BP CtlvI3
GO:0060349 bone morphogenesis 227 2 0.12 0.00658 6.58e-03 BP CtlvI3
GO:0060700 regulation of ribonuclease activity 13 2 0.01 8.4e-05 8.40e-05 BP CtlvI1
GO:0060836 lymphatic endothelial cell differentiati… 12 1 0.01 0.00643 6.43e-03 BP CtlvI3
GO:0061614 pri-miRNA transcription by RNA polymeras… 116 1 0.00 0.0027 2.70e-03 BP I1vI3
GO:0061626 pharyngeal arch artery morphogenesis 18 1 0.01 0.00963 9.63e-03 BP CtlvI3
GO:0071222 cellular response to lipopolysaccharide 438 4 0.46 0.00116 1.16e-03 BP CtlvI1
GO:0072583 clathrin-dependent endocytosis 166 2 0.09 0.00359 3.59e-03 BP CtlvI3
GO:0080008 Cul4-RING E3 ubiquitin ligase complex 128 1 0.00 0.0028 2.80e-03 CC CtlvI14
GO:0080008 Cul4-RING E3 ubiquitin ligase complex 128 1 0.00 0.0028 2.80e-03 CC I5vI14
GO:0097487 multivesicular body, internal vesicle 11 1 0.01 0.0070 7.00e-03 CC CtlvI3
GO:0099400 caveola neck 5 1 0.00 0.0032 3.20e-03 CC CtlvI3
GO:1900245 positive regulation of MDA-5 signaling p… 20 3 0.02 1.2e-06 1.20e-06 BP CtlvI1
GO:1900245 positive regulation of MDA-5 signaling p… 20 3 0.01 1.5e-07 2.00e-07 BP CtlvI3
GO:1900246 positive regulation of RIG-I signaling p… 31 3 0.03 4.8e-06 4.80e-06 BP CtlvI1
GO:1900246 positive regulation of RIG-I signaling p… 31 3 0.02 6.0e-07 6.00e-07 BP CtlvI3
GO:1901727 positive regulation of histone deacetyla… 17 1 0.01 0.00910 9.10e-03 BP CtlvI3
GO:1901953 positive regulation of anterograde dense… 9 1 0.01 0.00942 9.42e-03 BP CtlvI1
GO:1902499 positive regulation of protein autoubiqu… 9 1 0.00 0.00483 4.83e-03 BP CtlvI3
GO:1902731 negative regulation of chondrocyte proli… 5 1 0.00 0.00268 2.68e-03 BP CtlvI3
GO:1902823 negative regulation of late endosome to … 7 1 0.00 0.00376 3.76e-03 BP CtlvI3
GO:1903125 negative regulation of thioredoxin perox… 5 1 0.00 0.00268 2.68e-03 BP CtlvI3
GO:1903217 negative regulation of protein processin… 8 1 0.00 0.00429 4.29e-03 BP CtlvI3
GO:1903744 positive regulation of anterograde synap… 9 1 0.01 0.00942 9.42e-03 BP CtlvI1
GO:1903980 positive regulation of microglial cell a… 12 1 0.01 0.00643 6.43e-03 BP CtlvI3
GO:1904713 beta-catenin destruction complex binding 7 1 0.00 0.00457 4.57e-03 MF CtlvI3
GO:1904887 Wnt signalosome assembly 5 1 0.00 0.00268 2.68e-03 BP CtlvI3
GO:1905279 regulation of retrograde transport, endo… 8 1 0.00 0.00429 4.29e-03 BP CtlvI3
GO:1905289 regulation of CAMKK-AMPK signaling casca… 10 1 0.01 0.00536 5.36e-03 BP CtlvI3
GO:2000051 negative regulation of non-canonical Wnt… 17 3 0.02 7.3e-07 7.00e-07 BP CtlvI1
GO:2000172 regulation of branching morphogenesis of… 18 1 0.01 0.00963 9.63e-03 BP CtlvI3
GO:2000553 positive regulation of T-helper 2 cell c… 18 3 0.02 8.8e-07 9.00e-07 BP CtlvI1
GO:2001014 regulation of skeletal muscle cell diffe… 62 2 0.03 0.00051 5.10e-04 BP CtlvI3
GO:2001014 regulation of skeletal muscle cell diffe… 62 1 0.00 0.0014 1.40e-03 BP I1vI3